A transfer learning approach for network modeling

Abstract

Network models have been widely used in many subject areas to characterize the interactions between physical entities. A typical problem is to identify the network for multiple related tasks that share some similarities. In this case, a transfer learning approach that can leverage the knowledge gained during the modeling of one task to help better model another task is highly desirable. This article proposes a transfer learning approach that adopts a Bayesian hierarchical model framework to characterize the relatedness between tasks and additionally uses L 1-regularization to ensure robust learning of the networks with limited sample sizes. A method based on the Expectation–Maximization (EM) algorithm is further developed to learn the networks from data. Simulation studies are performed that demonstrate the superiority of the proposed transfer learning approach over single-task learning that learns the network of each task in isolation. The proposed approach is also applied to identify brain connectivity networks associated with Alzheimer’s Disease (AD) from functional magnetic resonance image data. The findings are consistent with the AD literature.